DeepAI AI Chat
Log In Sign Up

Echo State Network for two-dimensional turbulent moist Rayleigh-Bénard convection

by   Florian Heyder, et al.

Recurrent neural networks are machine learning algorithms which are suited well to predict time series. Echo state networks are one specific implementation of such neural networks that can describe the evolution of dynamical systems by supervised machine learning without solving the underlying nonlinear mathematical equations. In this work, we apply an echo state network to approximate the evolution of two-dimensional moist Rayleigh-Bénard convection and the resulting low-order turbulence statistics. We conduct long-term direct numerical simulations in order to obtain training and test data for the algorithm. Both sets are pre-processed by a Proper Orthogonal Decomposition (POD) using the snapshot method to reduce the amount of data. The training data comprise long time series of the first 150 most energetic POD coefficients. The reservoir is subsequently fed by the data and results in predictions of future flow states. The predictions are thoroughly validated by the data of the original simulation. Our results show good agreement of the low-order statistics. This incorporates also derived statistical moments such as the cloud cover close to the top of the convection layer and the flux of liquid water across the domain. We conclude that our model is capable of learning complex dynamics which is introduced here by the tight interaction of turbulence with the nonlinear thermodynamics of phase changes between vapor and liquid water. Our work opens new ways for the dynamic parametrization of subgrid-scale transport in larger-scale circulation models.


page 7

page 10

page 11


Reservoir computing model of two-dimensional turbulent convection

Reservoir computing is applied to model the large-scale evolution and th...

Direct data-driven forecast of local turbulent heat flux in Rayleigh-Bénard convection

A combined convolutional autoencoder-recurrent neural network machine le...

Neural Echo State Network using oscillations of gas bubbles in water

In the framework of physical reservoir computing (RC), machine learning ...

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

This work proposes a machine-learning framework for modeling the error i...

Inferring Global Dynamics Using a Learning Machine

Given a segment of time series of a system at a particular set of parame...

Investigation of Proper Orthogonal Decomposition for Echo State Networks

Echo State Networks (ESN) are a type of Recurrent Neural Networks that y...